SIMuLDiTex: A Single Image Multiscale and Lightweight Diffusion Model for Texture Synthesis
SIMuLDiTex: Un modèle de diffusion léger, mono-image et multi-échelle pour la synthèse de texture.
Résumé
We introduce SIMuLDiTex, a Single Image Multi-scale & Light-weight Diffusion Texture model. Recent generative models, though effective, suffer from high computational costs and memory requirements. Our approach addresses these challenges by employing a coarse-to-fine strategy that accelerates sampling and maintains high-resolution fidelity without the need for pre-trained auto-encoders. We introduce a scale-conditioning mechanism that ensures spatial consistency across scales in the diffusion process using a compact U-Net with only one million parameters. Experiments demonstrate that SIMuLDiTex outperforms existing methods in speed, quality, and scalability, offering a practical solution for interactive-time and high-resolution texture generation. Code and models are available at https: //github.com/PierrickCh/SIMuLDiTex.
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